Man pages for takayasaito/precrec
Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves
| as.data.frame | Convert a curves and points object to a data frame |
| auc | Retrieve a data frame of AUC scores |
| auc_ci | Calculate CIs of ROC and precision-recall AUCs |
| autoplot | Plot performance evaluation measures with ggplot2 |
| B1000 | Balanced data with 1000 positives and 1000 negatives. |
| B500 | Balanced data with 500 positives and 500 negatives. |
| create_sim_samples | Create random samples for simulations |
| evalmod | Evaluate models and calculate performance evaluation measures |
| format_nfold | Create n-fold cross validation dataset from data frame |
| fortify | Convert a curves and points object to a data frame for... |
| IB1000 | Imbalanced data with 1000 positives and 10000 negatives. |
| IB500 | Imbalanced data with 500 positives and 5000 negatives. |
| join_labels | Join observed labels of multiple test datasets into a list |
| join_scores | Join scores of multiple models into a list |
| M2N50F5 | 5-fold cross validation sample. |
| mmdata | Reformat input data for performance evaluation calculation |
| P10N10 | A small example dataset with several tied scores. |
| part | Calculate partial AUCs |
| pauc | Retrieve a data frame of pAUC scores |
| plot | Plot performance evaluation measures |
| precrec | precrec: A package for computing accurate ROC and... |
